Fine-tuning transfer learning based on DCGAN integrated with self-attention and spectral normalization for bearing fault diagnosis
In the current big-data context of Industry 4.0, insufficient training data has become a major
bottleneck in developing data-driven diagnosis approaches, restricting the accuracy of deep …
bottleneck in developing data-driven diagnosis approaches, restricting the accuracy of deep …
Detection of the pipeline elbow erosion by percussion and deep learning
Elbows are commonly used in pipelines to change the direction of flow, and the pipeline
elbows are prone to erosion caused by the transported medium. Detection of the pipeline …
elbows are prone to erosion caused by the transported medium. Detection of the pipeline …
Multivariate dynamic mode decomposition and its application to bearing fault diagnosis
In practical engineering applications, the multivariate signal contains more fault feature
information than the single-channel signal. How to realize synchronous extraction of fault …
information than the single-channel signal. How to realize synchronous extraction of fault …
Fault diagnosis of planetary gears based on intrinsic feature extraction and deep transfer learning
The planetary gearbox is a key transmission apparatus used to change speed and torque.
The planetary gear is one of the most failure-prone components in a planetary gearbox. Due …
The planetary gear is one of the most failure-prone components in a planetary gearbox. Due …
A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet
Rolling bearings play a crucial role as components in mechanical equipment.
Malfunctioning rolling bearings can disrupt the normal operation of the equipment and pose …
Malfunctioning rolling bearings can disrupt the normal operation of the equipment and pose …
ResNet-integrated very early bolt looseness monitoring based on intrinsic feature extraction of percussion sounds
Very early bolt looseness monitoring has been a challenge in the field of structural health
monitoring. The authors have conducted a further study of the previous researches, with the …
monitoring. The authors have conducted a further study of the previous researches, with the …
A novel multivariate cutting force-based tool wear monitoring method using one-dimensional convolutional neural network
Tool wear condition monitoring during the machining process is one of the most important
considerations in precision manufacturing. Cutting force is one of the signals that has been …
considerations in precision manufacturing. Cutting force is one of the signals that has been …
Degradation tracking of rolling bearings based on local polynomial phase space warping
The condition monitoring of rolling bearings has received much attention in prognostics and
health management. Real-time monitoring of the bearings' degradation provides vital …
health management. Real-time monitoring of the bearings' degradation provides vital …
Fault location of distribution network based on back propagation neural network optimization algorithm
C Zhou, S Gui, Y Liu, J Ma, H Wang - Processes, 2023 - mdpi.com
Research on fault diagnosis and positioning of the distribution network (DN) has always
been an important research direction related to power supply safety performance. The back …
been an important research direction related to power supply safety performance. The back …
Improved two-dimensional multiscale fractional dispersion entropy: A novel health condition indicator for fault diagnosis of rolling bearings
The multiscale dispersion entropy (MDE), which measures the irregularity or chaos of 1-D
univariate time series through a dispersion pattern, is a useful tool to extract features from …
univariate time series through a dispersion pattern, is a useful tool to extract features from …